v0.2.0

What's New

Lazy Selective Rewind

@with_pool now defers per-type checkpoints until first acquire! and rewinds only the pools actually touched, instead of all 8 fixed-slot types. Up to 6.5× faster in common patterns with helper functions.

Bitmask-Aware Type Tracking

Per-type bitmask tracking replaces the boolean _untracked_flags system. When helper functions acquire types already tracked by the macro, the fast typed path is preserved via subset check (untracked ⊆ tracked).

Internal Naming Refactor

Internal identifiers renamed to reflect lazy-selective-rewind architecture (e.g. _mark_untracked! → _record_type_touch!, _depth_only_checkpoint! → _lazy_checkpoint!). Magic hex literals replaced with named constants. No user-facing API changes.

What's Changed

• (feat): Bitmask-Aware Untracked Tracking for @with_pool by @mgyoo86 in #16
• (perf): Dynamic Selective Rewind & Typed-Fallback Optimization by @mgyoo86 in #17
• (refactor): rename internals to match evolved architecture by @mgyoo86 in #18

Full Changelog: v0.1.2...v0.2.0

v0.1.2

What's New

⚠️ Breaking: Unified Bit Type API

acquire!(pool, Bit, n) now returns BitVector instead of SubArray{Bool}.

Why: Native BitVector utilizes SIMD-optimized chunk algorithms, making operations like count(), sum(), and bitwise broadcasting 10×–100× faster compared to SubArray{Bool} views.

# Before (v0.1.1): Returned SubArray{Bool}
# After  (v0.1.2): Returns native BitVector (SIMD optimized)

@with_pool pool function foo()
    bv = acquire!(pool, Bit, 10_000)

    # Operations using packed bits are significantly faster
    c = count(bv)  # 10x~100x speedup vs view behavior
end

Migration: No code changes needed for typical usage. Only affects code explicitly type-checking for SubArray.

What's Changed

• ⚠️ (refac): return BitVector for performance by @mgyoo86 in #15

Full Changelog: v0.1.1...v0.1.2

v0.1.1

What's New

BitVector Pooling

Memory-efficient pooling for boolean arrays using Julia's native BitVector storage. Reduces memory usage by ~8x compared to Vector{Bool}.

@with_pool pool begin
    # Packed storage (1 bit per element)
    mask = acquire!(pool, Bit, 1024)

    # N-dimensional support
    grid = acquire!(pool, Bit, 128, 128)
end

Convenience Functions

New trues! and falses! functions for drop-in replacement of Base allocations:

@with_pool pool begin
    mask = falses!(pool, 1024)      # All false
    flags = trues!(pool, 10, 10)    # All true, 2D
end

These mirror Julia's trues() and falses() with pooled memory management.

Memory Comparison

Type	Storage	Memory (1024 elements)
Vector{Bool}	1 byte/element	1024 bytes
BitVector	1 bit/element	128 bytes

What's Changed

• (feat): BitVector Pooling Support by @mgyoo86 in #14

Full Changelog: v0.1.0...v0.1.1

v0.1.0

What's Changed

• Add unsafe_acquire! API and Optimize N-D Array Performance by @mgyoo86 in #1
• #N-D Cache for Zero-Alloction by @mgyoo86 in #2
• Export checkpoint! and rewind! for Manual Pool Management by @mgyoo86 in #3
• Return ReshapedArray from acquire! for N-D arrays by @mgyoo86 in #4
• PR: N-way Cache for unsafe_acquire! by @mgyoo86 in #5
• Untracked Acquire Detection & State Management Improvements by @mgyoo86 in #6
• Add Fixed Slot Infrastructure by @mgyoo86 in #7
• Add reset! function and safe rewind! behavior by @mgyoo86 in #8
• Add CUDA Backend Support by @mgyoo86 in #9
• Convenience Functions & DisabledPool by @mgyoo86 in #10
• Improve @with_pool Macro Source Location for Better Coverage & Debugging by @mgyoo86 in #11
• docs: add GitHub Pages documentation with Documenter.jl by @mgyoo86 in #12

New Contributors

• @mgyoo86 made their first contribution in #1

Full Changelog: https://github.com/ProjectTorreyPines/AdaptiveArrayPools.jl/commits/v0.1.0